The Brain Drain Potential of Students in the African Health and Nonhealth Sectors
Bibliographic record
Abstract
The departure of health professionals to Europe and North America is placing an intolerable burden on public health systems in many African countries. Various retention, recall, and replacement policies to ameliorate the impact of this brain drain have been suggested, none of which have been particularly successful to date. The key question for the future is whether the brain drain of health sector skills is likely to continue and whether the investment of African countries in training health professionals will continue to be lost through emigration. This paper examines the emigration intentions of trainee health professionals in six Southern African countries. The data was collected by the Southern African Migration Program (SAMP) in a survey of final-year students across the region which included 651 students training for the health professions. The data also allows for the comparison of health sector with other students. The analysis presented in this paper shows very high emigration potential amongst all final-year students. Health sector students do show a slightly higher inclination to leave than those training to work in other sectors. These findings present a considerable challenge for policy makers seeking to encourage students to stay at home and work after graduation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".